Two-model Active Learning Approach for Inappropriate Information Classification in Social Networks

The work process of specialists in protection from information consists of many time-consuming tasks, including data collection, datasets formation, and data manual labelling. In this paper, we attempted to help such specialists with a two-model approach based on the iterative online training of binary classifiers. This approach is used for inappropriate information detection and applied on text posts from the VKontakte social network. The first model is used to detect text posts that are corresponding to the selected topic and is trained on the data that is labelled positively and negatively by experts as well as random text data. The second model is used to improve the accuracy of the first model and is trained only on the data that is labelled by the experts. The novelty of the approach lies in the constantly growing dataset, while the classifiers training process takes place during the operator's work. The approach works with texts of any size and content and applicable for Russian social networks. The research contribution lies in the original approach for inappropriate information detection. The practical significance of the approach lies in the automation of routine tasks to reduce the burden on specialists in the area of protection from information. Experimental evaluation of the approach is focused on its iterative retraining part. For the experiment, text posts of different topics from the VKontakte social network were collected and labelled. Those topics include: Aggression, Dangerous conspiracy theories, Radicalism, Gambling, Prostitution, and Sects. After that, we have evaluated precision, recall, F-measure and ROC-AUC metrics for classifiers trained on random subsamples of different sizes and different topics. Those metrics were evaluated for both one-model and two-model implementations of the approach, while the following classifiers were used: linear support vector machine, passive-aggressive classifier, multilayer perceptron. Moreover, the advantages and disadvantages of the approach, as well as future work directions, were indicated.